Electronics case study: ₹1L to ₹1.2L monthly revenue in 2–3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   The first problem in this scenario is turning visits into orders, so the opening month on this smaller store goes there. Before budget rises, we close the gaps that stop a visitor buying: missing reviews, an unclear return window, no about page and no prepaid incentive. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a smaller electronics brand grows monthly revenue in 2–3 months, from ₹1L to ₹10L–₹15L+ (12.5x). That is faster than nine in ten of our measured accounts grew over the same time, so the case study below is on what that fastest tenth reached and shows where it lands against the number this scenario calls for. The main problem in this scenario is turning visits into orders, with competition close behind. Every rupee of ads is capped by how well the store turns visits into orders. Each extra item in a basket is revenue the ad has already paid for.

   How it works

   The fix list goes to the brand's team in the opening weeks, ahead of any budget step. The tiers follow real order values, not round numbers.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We keep a shared daily sheet with the ad platform's revenue next to real store orders. We set the path from ₹1L to ₹10L–₹15L+ as written monthly numbers, and read every review against the month so far. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   No starting return on ad spend was given; the starting return is what measured electronics stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed month shows up early instead of at the end of the case study.

   How it works

   The sheet is read before each budget change. Written monthly numbers make each scaling decision explicit. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We test with video, static image and catalogue ads first, rising to about two dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We run every lead product in a campaign of its own, read every week. We run creator, customer-feedback and founder-led video. We test several interest clusters against a broad audience, with video and catalogue ads.

   Why

   A product nobody buys is visible within a week when it has its own campaign. Trust-carrying video held return in most measured accounts. Buyers split by use case, so clusters show which one buys before budget is committed.

   How it works

   A product that does not sell is paused inside the week. Weak UGC is swapped for founder-led video rather than scaled. Clusters are read against broad before the scaling budget is set. The number of new ads grows with spend; video makes up the largest part and static image the next.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹10L–₹15L+: the budget rises gently while return holds, and Facebook Feed carries the most spend and Instagram Reels the next. We refresh tired ads by mixing old and new creatives as spend rises. We set spend against stock with a product-level stock count, and scale with bundle offers and a cost-control campaign.

   Why

   In Month 2 to Month 3 the budget rises gently, while return on spend holds. Two of these months stop their budget step where return slips too far. Each budget step here is sized so that return stays close to where it was. Frequency climbs with spend, and tired ads lose click-through first.

   How it works

   Proven ads stay while new ones are added. Spend is set against stock by product. Facebook Feed carries the largest share of spend in the final month, with Instagram Reels next. Most spend reaches people who have not bought yet; past visitors return more per rupee.

1. Month 1

   The learning phase opens: a small daily budget carries the first ads, and store orders are matched to tracking. Cart-value offers go live at checkout. Store fixes go live: reviews, trust pointers and the prepaid offer.

   • Revenue ₹1L
   • ROAS 1.92x
   • Ad spend ₹54,502
2. Month 2

   Scaling begins: bundle offers run with a cost-control campaign. Budget is raised only as far as return allows: return holds as the budget steps up.

   • Revenue ₹1.2L
   • ROAS 1.95x
   • Ad spend ₹62,074
3. Month 3

   A fresh round of creator and customer-feedback video goes live. Budget is raised only as far as return allows: return holds as the budget holds. The case study finishes short of ₹10L–₹15L+: budget stops rising where return starts to slip. Cost per purchase ends close to the learning phase.

   • Revenue ₹1.2L
   • ROAS 1.95x
   • Ad spend ₹63,951

1. 1

   Website fixes: shop-by-phone-brand menu, reviews and trust pointers on product pages

2. 2

   Ask for a product-level festive stock count so spend follows stock.

3. 3

   Kept a creatives bank and 4–8 social posts a month